Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.4 KiB
Average record size in memory64.3 B

Variable types

Numeric8

Warnings

station has unique values Unique
lat has unique values Unique
lng has unique values Unique
geoid has unique values Unique

Reproduction

Analysis started2021-05-03 01:05:08.075309
Analysis finished2021-05-03 01:12:51.905922
Duration7 minutes and 43.83 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

station
Real number (ℝ≥0)

UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1181.836
Minimum1
Maximum1569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:52.048545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile130.9
Q11102.5
median1261.5
Q31423.25
95-th percentile1540.05
Maximum1569
Range1568
Interquartile range (IQR)320.75

Descriptive statistics

Standard deviation374.8967167
Coefficient of variation (CV)0.3172155161
Kurtosis3.31985844
Mean1181.836
Median Absolute Deviation (MAD)161
Skewness-1.973467698
Sum590918
Variance140547.5482
MonotocityNot monotonic
2021-05-02T21:12:52.220121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10241
 
0.2%
14261
 
0.2%
14411
 
0.2%
14391
 
0.2%
14381
 
0.2%
14371
 
0.2%
14361
 
0.2%
14351
 
0.2%
14331
 
0.2%
14321
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
11
0.2%
101
0.2%
111
0.2%
121
0.2%
141
0.2%
ValueCountFrequency (%)
15691
0.2%
15681
0.2%
15671
0.2%
15661
0.2%
15651
0.2%

lat
Real number (ℝ≥0)

UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.31119048
Minimum30.03024035
Maximum39.98665516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:52.404625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30.03024035
5-th percentile30.74124546
Q133.01165918
median35.49866644
Q337.73823063
95-th percentile39.56235719
Maximum39.98665516
Range9.95641481
Interquartile range (IQR)4.726571443

Descriptive statistics

Standard deviation2.811239498
Coefficient of variation (CV)0.07961327443
Kurtosis-1.134837947
Mean35.31119048
Median Absolute Deviation (MAD)2.379393085
Skewness-0.09642132679
Sum17655.59524
Variance7.903067515
MonotocityNot monotonic
2021-05-02T21:12:52.580145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.389277181
 
0.2%
31.357656291
 
0.2%
37.501121441
 
0.2%
30.978054481
 
0.2%
39.548101661
 
0.2%
37.005192091
 
0.2%
38.834045731
 
0.2%
35.223466311
 
0.2%
36.831687861
 
0.2%
30.74657181
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
30.030240351
0.2%
30.053653261
0.2%
30.096046811
0.2%
30.101168851
0.2%
30.135366781
0.2%
ValueCountFrequency (%)
39.986655161
0.2%
39.982603171
0.2%
39.976419471
0.2%
39.970430891
0.2%
39.928273871
0.2%

lng
Real number (ℝ)

UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-88.40994379
Minimum-99.90351721
Maximum-74.69559264
Zeros0
Zeros (%)0.0%
Negative500
Negative (%)100.0%
Memory size4.0 KiB
2021-05-02T21:12:52.750664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-99.90351721
5-th percentile-98.77882417
Q1-94.23691872
median-88.48051502
Q3-82.84815593
95-th percentile-76.80850422
Maximum-74.69559264
Range25.20792457
Interquartile range (IQR)11.38876279

Descriptive statistics

Standard deviation6.917512811
Coefficient of variation (CV)-0.07824360603
Kurtosis-1.093432973
Mean-88.40994379
Median Absolute Deviation (MAD)5.724065135
Skewness0.1116124007
Sum-44204.97189
Variance47.85198349
MonotocityNot monotonic
2021-05-02T21:12:52.936171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-93.0507661
 
0.2%
-88.07956861
 
0.2%
-96.455251661
 
0.2%
-83.248248161
 
0.2%
-99.585137821
 
0.2%
-88.826308091
 
0.2%
-76.361494761
 
0.2%
-93.661234011
 
0.2%
-90.570762491
 
0.2%
-96.84452641
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
-99.903517211
0.2%
-99.824950391
0.2%
-99.8004111
0.2%
-99.755377281
0.2%
-99.746533181
0.2%
ValueCountFrequency (%)
-74.695592641
0.2%
-74.696177331
0.2%
-75.177217281
0.2%
-75.324223761
0.2%
-75.439867871
0.2%

geoid
Real number (ℝ≥0)

UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.94852942 × 1014
Minimum1.0030101 × 1013
Maximum5.40919648 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:53.113725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.0030101 × 1013
5-th percentile1.107050395 × 1013
Q12.00363028 × 1014
median2.90398702 × 1014
Q34.501921773 × 1014
95-th percentile5.10740436 × 1014
Maximum5.40919648 × 1014
Range5.30889547 × 1014
Interquartile range (IQR)2.498291493 × 1014

Descriptive statistics

Standard deviation1.545291824 × 1014
Coefficient of variation (CV)0.5240889961
Kurtosis-1.066633698
Mean2.94852942 × 1014
Median Absolute Deviation (MAD)1.18469136 × 1014
Skewness-0.1899108044
Sum1.47426471 × 1017
Variance2.387926821 × 1028
MonotocityNot monotonic
2021-05-02T21:12:53.302190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.70299501 × 10141
 
0.2%
2.80639501 × 10141
 
0.2%
2.90630801 × 10141
 
0.2%
2.01110008 × 10141
 
0.2%
2.80559501 × 10141
 
0.2%
5.4087963 × 10141
 
0.2%
4.71339506 × 10141
 
0.2%
4.50850018 × 10141
 
0.2%
3.70499603 × 10141
 
0.2%
3.71419203 × 10141
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
1.0030101 × 10131
0.2%
1.0030104 × 10131
0.2%
1.0059502 × 10131
0.2%
1.0059505 × 10131
0.2%
1.007010003 × 10131
0.2%
ValueCountFrequency (%)
5.40919648 × 10141
0.2%
5.4087963 × 10141
0.2%
5.40859625 × 10141
0.2%
5.40839659 × 10141
0.2%
5.40759603 × 10141
0.2%

state
Real number (ℝ≥0)

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.378
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:53.475750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q120
median29
Q345
95-th percentile51
Maximum54
Range53
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.43618046
Coefficient of variation (CV)0.5254333333
Kurtosis-1.067503709
Mean29.378
Median Absolute Deviation (MAD)12
Skewness-0.1911321125
Sum14689
Variance238.2756673
MonotocityNot monotonic
2021-05-02T21:12:53.639287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4858
 
11.6%
2941
 
8.2%
3737
 
7.4%
2835
 
7.0%
2032
 
6.4%
4032
 
6.4%
1332
 
6.4%
131
 
6.2%
527
 
5.4%
2126
 
5.2%
Other values (13)149
29.8%
ValueCountFrequency (%)
131
6.2%
527
5.4%
102
 
0.4%
1211
 
2.2%
1332
6.4%
ValueCountFrequency (%)
5415
 
3.0%
5119
 
3.8%
4858
11.6%
4723
 
4.6%
4516
 
3.2%

county
Real number (ℝ≥0)

Distinct135
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.702
Minimum1
Maximum810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:53.827782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q141
median89
Q3141
95-th percentile301.1
Maximum810
Range809
Interquartile range (IQR)100

Descriptive statistics

Standard deviation92.91826949
Coefficient of variation (CV)0.8708203173
Kurtosis9.272092362
Mean106.702
Median Absolute Deviation (MAD)50
Skewness2.291777857
Sum53351
Variance8633.804806
MonotocityNot monotonic
2021-05-02T21:12:54.010293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7713
 
2.6%
111
 
2.2%
39
 
1.8%
1339
 
1.8%
639
 
1.8%
59
 
1.8%
418
 
1.6%
478
 
1.6%
218
 
1.6%
1198
 
1.6%
Other values (125)408
81.6%
ValueCountFrequency (%)
111
2.2%
39
1.8%
59
1.8%
73
 
0.6%
94
 
0.8%
ValueCountFrequency (%)
8101
0.2%
5032
0.4%
4871
0.2%
4711
0.2%
4571
0.2%

tract
Real number (ℝ≥0)

Distinct299
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592200.96
Minimum100
Maximum990200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:54.192336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile1800.95
Q150478
median935654.5
Q3955100
95-th percentile970800.05
Maximum990200
Range990100
Interquartile range (IQR)904622

Descriptive statistics

Standard deviation429690.8466
Coefficient of variation (CV)0.7255828268
Kurtosis-1.707153512
Mean592200.96
Median Absolute Deviation (MAD)37295.5
Skewness-0.4408795899
Sum296100480
Variance1.846342236 × 1011
MonotocityNot monotonic
2021-05-02T21:12:54.369862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95010025
 
5.0%
95020020
 
4.0%
95040015
 
3.0%
95030013
 
2.6%
96010011
 
2.2%
9603008
 
1.6%
9505008
 
1.6%
9602006
 
1.2%
9701006
 
1.2%
9705005
 
1.0%
Other values (289)383
76.6%
ValueCountFrequency (%)
1004
0.8%
2002
0.4%
3003
0.6%
4002
0.4%
7002
0.4%
ValueCountFrequency (%)
9902002
0.4%
9901001
 
0.2%
9900003
0.6%
9801002
0.4%
9799001
 
0.2%

block
Real number (ℝ≥0)

Distinct343
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2113.538
Minimum1
Maximum6069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-05-02T21:12:54.540405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1004
Q11063
median2028
Q33015
95-th percentile4107.6
Maximum6069
Range6068
Interquartile range (IQR)1952

Descriptive statistics

Standard deviation1169.818872
Coefficient of variation (CV)0.5534884501
Kurtosis0.6027893061
Mean2113.538
Median Absolute Deviation (MAD)973.5
Skewness0.9774052511
Sum1056769
Variance1368476.193
MonotocityNot monotonic
2021-05-02T21:12:54.724910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30006
 
1.2%
10056
 
1.2%
10035
 
1.0%
10005
 
1.0%
10485
 
1.0%
20904
 
0.8%
10094
 
0.8%
10044
 
0.8%
20024
 
0.8%
10064
 
0.8%
Other values (333)453
90.6%
ValueCountFrequency (%)
12
0.4%
21
0.2%
61
0.2%
141
0.2%
191
0.2%
ValueCountFrequency (%)
60691
0.2%
60501
0.2%
60361
0.2%
60301
0.2%
54961
0.2%

Interactions

2021-05-02T21:05:13.063270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:05:24.170156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:05:35.192521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:05:52.845819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:06:04.498507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:06:17.488786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:06:33.094981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:06:50.499454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:06:55.382029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:06:55.531629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:01.759610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:01.974389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:03.302984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:06.185736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:09.227607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:13.671967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:13.801672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:19.941965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:20.174323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:21.483061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:24.412525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:27.496284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:07:46.491182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:08:01.355188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:08:16.919432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:08:31.929210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:08:48.742556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:06.396472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:24.412353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:29.259587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:29.508711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:29.739138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:36.331425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:37.674368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:41.171478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:44.954882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:53.032645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:56.253030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:09:59.651461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:09.223881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:12.497136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:19.058128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:25.719335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:38.805912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:46.550718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:10:54.319970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:11:08.495620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:11:16.277813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:11:25.295201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:11:36.344228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:11:49.419217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:11:57.873205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:12:06.336092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:12:21.694012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:12:30.299230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-02T21:12:39.978828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-02T21:12:54.910417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-02T21:12:55.084948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-02T21:12:55.246521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-02T21:12:55.408125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-02T21:12:51.490036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-02T21:12:51.775273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

stationlatlnggeoidstatecountytractblock
0133.626177-98.0665934807703030210364877303021036
11037.786761-98.0927242015500180011142015518001114
2100036.667908-77.008185511752004002068511752004002068
3100133.162642-98.698637485039504002141485039504002141
4100231.811688-95.10591048073950801303648739508013036
5100333.468272-83.790157132171002011043132171002011043
6100436.810252-77.073923511752001002052511752001002052
7100630.926817-85.55241512059960100107212599601001072
8100734.613684-82.802887450070107006069457107006069
9100833.597874-96.106257481479505002059481479505002059

Last rows

stationlatlnggeoidstatecountytractblock
490155838.215510-88.7264831708105040020751781504002075
491156033.908145-94.230955513308040020255133804002025
492156135.726361-99.534239401299600002117401299600002117
493156236.931543-94.45452929145020602103629145206021036
494156436.978617-94.20460129145020400505529145204005055
495156537.236228-82.537498211959309002009211959309002009
496156637.353235-93.458478290770050022044297750022044
497156735.444186-95.975046401110009024139401119024139
498156835.534000-97.82845440017300801408240173008014082
499156939.279579-80.08508954091964800504954919648005049